Diabetes prediction using machine learning involves developing models to forecast diabetes onset based on patient data like age, BMI, blood pressure, and glucose levels. Techniques include logistic regression, decision trees, and neural networks, enhancing early diagnosis and personalized treatment plans.
dataset used in this code in fetched from online website/api to work on the models
alternative option :- download the dataset and replace the code line with the file path of the dataset you downloaded (make sure you save the code file and dataset files in a same file location)
Diabetes Prediction using Machine Learning:-
This project aims to predict diabetes using various machine learning models. The dataset used is from the UCI Machine Learning Repository, containing medical data related to diabetes.
Dataset:-
The dataset includes the following features:
Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age,
The target variable is:
Outcome (0 or 1)
Getting Started
Prerequisites:-
Make sure you have the following libraries installed:
pandas, seaborn, matplotlib, scikit-learn,
Results:-
For each model, the following metrics are printed:
Actual output (list of true labels for the test set)
Predicted output (list of predicted labels for the test set)
Accuracy (model accuracy on the training set)
Contributing:-
Contributions are welcome! Please create a pull request or raise an issue to discuss any changes.